code: def __init__(
      self,
      dataset,
      batch_size=1,
      shuffle=True,
      sampler=None,
      batch_sampler=None,
      num_workers=0,
      collate_fn=None,
      pin_memory=False,
      drop_last=False,
      timeout=0,
      worker_init_fn=None,
  ):
      if collate_fn is None:
          collate_fn = no_collate
  
      if isinstance(dataset, CombinationMetaDataset) and (
          sampler is None
      ):
          if shuffle:
              sampler = CombinationRandomSampler(dataset)
          else:
              sampler = CombinationSequentialSampler(dataset)
          shuffle = False
  
      super(MetaDataLoader, self).__init__(
          dataset,
          batch_size=batch_size,
          shuffle=shuffle,
          sampler=sampler,
          batch_sampler=batch_sampler,
          num_workers=num_workers,
          collate_fn=collate_fn,
          pin_memory=pin_memory,
          drop_last=drop_last,
          timeout=timeout,
          worker_init_fn=worker_init_fn,
      )
  
file_path: pytorch-meta/torchmeta/utils/data/dataloader.py
imports: from collections import OrderedDict
  from torch.utils.data import DataLoader
  from torch.utils.data.dataloader import default_collate
  from torch.utils.data.dataset import Dataset
  from torchmeta.utils.data.dataset import CombinationMetaDataset
  from torchmeta.utils.data.sampler import CombinationSequentialSampler, CombinationRandomSampler
label_desc: Use image node_id 1 to create a new MetaDataLoader object from inherited base classes: DataLoader with example: obj = MetaDataLoader(dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn)